| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1 - What is Data Science English.vtt | 7.7 KB | ||
| 1 - What is Data Science.mp4 | 34.7 MB | ||
| 10 - Project Customer Segmentation using Clustering | |||
| 11 - Project Fake News Detection using machine learning Python | |||
| 12 - Project COVID19 Coronavirus Infection Probability using machine learning | |||
| 13 - Miscellaneous | |||
| 14 - Python Crash Course | |||
| 2 - Installation and Colab | |||
| 3 - Python for Data Analysis Numpy | |||
| 4 - Python for Data Analysis Pandas | |||
| 10 - Pandas DataFrames English.vtt | 10.4 KB | ||
| 10 - Pandas DataFrames.mp4 | 44.5 MB | ||
| 11 - Grouping and Filtering English.vtt | 8.4 KB | ||
| 11 - Grouping and Filtering.mp4 | 35.4 MB | ||
| 12 - Slicing and Sorting English.vtt | 6.9 KB | ||
| 12 - Slicing and Sorting.mp4 | 26.9 MB | ||
| 13 - Pandas Missing Values English.vtt | 10.5 KB | ||
| 13 - Pandas Missing Values.mp4 | 47.8 MB | ||
| 13 - pandas-flightsexample.ipynb | 60.9 KB | ||
| 14 - Pandas Aggregation Functions English.vtt | 7.3 KB | ||
| 14 - Pandas Aggregation Functions.mp4 | 29.5 MB | ||
| 14 - pandas-aggregate.ipynb | 20 KB | ||
| 5 - Python for Data Visualization Matplotlib | |||
| 15 - Matplotlib Introduction English.vtt | 12.1 KB | ||
| 15 - Matplotlib Introduction.mp4 | 45.5 MB | ||
| 16 - Matplotlib Bar Graphs English.vtt | 5.1 KB | ||
| 16 - Matplotlib Bar Graphs.mp4 | 24.9 MB | ||
| 16 - matplotlib-bargraph-histogram.ipynb | 95.5 KB | ||
| 17 - Matplotlib Histogram English.vtt | 3.4 KB | ||
| 17 - Matplotlib Histogram.mp4 | 15.6 MB | ||
| 18 - Matplotlib Scatter Plot English.vtt | 5.3 KB | ||
| 18 - Matplotlib Scatter Plot.mp4 | 18.9 MB | ||
| 18 - matplotliob-scatterplot.ipynb | 32.5 KB | ||
| 19 - Matplotlib Area Plot English.vtt | 3.9 KB | ||
| 19 - Matplotlib Area Plot.mp4 | 16.2 MB | ||
| 19 - area-plot.ipynb | 21.2 KB | ||
| 20 - Matplotlib Pie Chart English.vtt | 2.7 KB | ||
| 20 - Matplotlib Pie Chart.mp4 | 9.8 MB | ||
| 20 - pie-chart.ipynb | 14.1 KB | ||
| 21 - Matplotlib Subplots English.vtt | 4.4 KB | ||
| 21 - Matplotlib Subplots.mp4 | 12.7 MB | ||
| 6 - Supervised Machine Learning | |||
| 22 - What is Supervised Machine Learning and Linear Regression Algorithm English.vtt | 7.7 KB | ||
| 22 - What is Supervised Machine Learning and Linear Regression Algorithm.mp4 | 42 MB | ||
| 23 - Implementation of Linear Regression English.vtt | 8.8 KB | ||
| 23 - Implementation of Linear Regression.mp4 | 42 MB | ||
| 23 - salaryData.csv | 409.6 B | ||
| 23 - simple-linear-regression.ipynb | 69.8 KB | ||
| 24 - Plot Linear Regression English.vtt | 6.6 KB | ||
| 24 - Plot Linear Regression.mp4 | 28.9 MB | ||
| 25 - Implementation of Multi Linear Regression English.vtt | 5.5 KB | ||
| 25 - Implementation of Multi Linear Regression.mp4 | 29 MB | ||
| 25 - home.csv | 204.8 B | ||
| 25 - multi-linear-regression.ipynb | 23.8 KB | ||
| 26 - Example 2 Multilinear Regression English.vtt | 7.1 KB | ||
| 26 - Example 2 Multilinear Regression.mp4 | 28.7 MB | ||
| 26 - marks.csv | 102.4 B | ||
| 26 - multi-linear-regression-ex2.ipynb | 15.9 KB | ||
| 27 - Use Case Boston Housing Price Prediction English.vtt | 13 KB | ||
| 27 - Use Case Boston Housing Price Prediction.mp4 | 82.3 MB | ||
| 27 - boston-house.ipynb | 36.1 KB | ||
| 28 - Save Model English.vtt | 7.4 KB | ||
| 28 - Save Model.mp4 | 36.3 MB | ||
| 28 - save-model.ipynb | 12 KB | ||
| 29 - bank.csv | 102.4 B | ||
| 29 - how to implement Logistic Regression English.vtt | 5.1 KB | ||
| 29 - how to implement Logistic Regression.mp4 | 26.3 MB | ||
| 29 - logistic-regression.ipynb | 19.5 KB | ||
| 30 - Logistic Regression on Iris Flower Dataset English.vtt | 10.1 KB | ||
| 30 - Logistic Regression on Iris Flower Dataset.mp4 | 58.8 MB | ||
| 31 - Logistic Regression on Digits Dataset English.vtt | 6.4 KB | ||
| 31 - Logistic Regression on Digits Dataset.mp4 | 43.3 MB | ||
| 32 - Naive Bayes Classifier English.vtt | 6.5 KB | ||
| 32 - Naive Bayes Classifier.mp4 | 33.6 MB | ||
| 33 - Implementation of Naive Bayes classifier on Wine Dataset English.vtt | 8.5 KB | ||
| 33 - Implementation of Naive Bayes classifier on Wine Dataset.mp4 | 45.6 MB | ||
| 33 - wine-dataset-example.ipynb | 12.4 KB | ||
| 34 - Introduction to Naive Bayes Classifier for Text Classification English.vtt | 3.5 KB | ||
| 34 - Introduction to Naive Bayes Classifier for Text Classification.mp4 | 12.1 MB | ||
| 34 - classifier-basic.ipynb | 2.9 KB | ||
| 35 - Implementating Naive Bayes Classifier for Text Classification English.vtt | 9.1 KB | ||
| 35 - Implementating Naive Bayes Classifier for Text Classification.mp4 | 40 MB | ||
| 35 - multinomial-classifier.ipynb | 10.5 KB | ||
| 36 - Introduction to Decision Tree English.vtt | 5.9 KB | ||
| 36 - Introduction to Decision Tree.mp4 | 33.4 MB | ||
| 37 - Implementation of Decision Tree English.vtt | 12.3 KB | ||
| 37 - Implementation of Decision Tree.mp4 | 77.6 MB | ||
| 37 - decision-tree.ipynb | 35.4 KB | ||
| 38 - KNearest NeighborKNN Algorithm English.vtt | 6.3 KB | ||
| 38 - KNearest NeighborKNN Algorithm.mp4 | 36.9 MB | ||
| 39 - Implementation of KNearest NeighborKNN Algorithm English.vtt | 7.1 KB | ||
| 39 - Implementation of KNearest NeighborKNN Algorithm.mp4 | 36.3 MB | ||
| 39 - User-Data.csv | 10.7 KB | ||
| 39 - knn-example.ipynb | 7.9 KB | ||
| 40 - Support Vector Machine Algorithm English.vtt | 8.4 KB | ||
| 40 - Support Vector Machine Algorithm.mp4 | 42.7 MB | ||
| 41 - Implementation of Support Vector Machine Algorithm English.vtt | 10.4 KB | ||
| 41 - Implementation of Support Vector Machine Algorithm.mp4 | 64.7 MB | ||
| 41 - SVM.ipynb | 196.2 KB | ||
| 42 - Random Forest Algorithm English.vtt | 5.1 KB | ||
| 42 - Random Forest Algorithm.mp4 | 43.9 MB | ||
| 43 - Implementation of Random Forest Algorithm English.vtt | 12 KB | ||
| 43 - Implementation of Random Forest Algorithm.mp4 | 59.9 MB | ||
| 43 - random-forest.ipynb | 7.7 KB | ||
| 7 - UnSupervised Machine Learning | |||
| 44 - What is UnSupervised Machine Learning English.vtt | 10 KB | ||
| 44 - What is UnSupervised Machine Learning.mp4 | 47.2 MB | ||
| 45 - Types of Unsupervised Learning English.vtt | 11.6 KB | ||
| 45 - Types of Unsupervised Learning.mp4 | 47.2 MB | ||
| 46 - Advantages and Disadvantages of Unsupervised Learning English.vtt | 7.3 KB | ||
| 46 - Advantages and Disadvantages of Unsupervised Learning.mp4 | 36.5 MB | ||
| 47 - What is clustering English.vtt | 9.1 KB | ||
| 47 - What is clustering.mp4 | 37.6 MB | ||
| 48 - Kmeans Clustering English.vtt | 10.7 KB | ||
| 48 - Kmeans Clustering.mp4 | 58.5 MB | ||
| 49 - Implementing Kmeans Clustering English.vtt | 8.7 KB | ||
| 49 - Implementing Kmeans Clustering.mp4 | 35.3 MB | ||
| 50 - Image compression using Kmeans clustering Color Quantization using KMeans English.vtt | 15.5 KB | ||
| 50 - Image compression using Kmeans clustering Color Quantization using KMeans.mp4 | 65.4 MB | ||
| 8 - Reinforcement Learning | |||
| 51 - Introduction to Reinforcement Learning English.vtt | 15.4 KB | ||
| 51 - Introduction to Reinforcement Learning.mp4 | 38.7 MB | ||
| 52 - Qlearning algorithm in machine learning English.vtt | 6.7 KB | ||
| 52 - Qlearning algorithm in machine learning.mp4 | 18.4 MB | ||
| 9 - Feature Engineering | |||
| 53 - What is Feature Engineering English.vtt | 6.8 KB | ||
| 53 - What is Feature Engineering.mp4 | 26.3 MB | ||
| 54 - What is Outlier English.vtt | 10.5 KB | ||
| 54 - What is Outlier.mp4 | 50.5 MB | ||
| 54 - heights.csv | 204.8 B | ||
| 54 - outlier-basic-IQR.ipynb | 63.8 KB | ||
| 55 - outlier detection and removal using std deviation English.vtt | 16.1 KB | ||
| 55 - outlier detection and removal using std deviation.mp4 | 66.2 MB | ||
| 55 - outlier-detection-and-removal-using-std-deviation.ipynb | 64.6 KB | ||
| 56 - Z score English.vtt | 9.1 KB | ||
| 56 - Z score.mp4 | 19.5 MB | ||
| 57 - IQR English.vtt | 8.7 KB | ||
| 57 - IQR.mp4 | 20.5 MB | ||
| 9 - Pandas Series English.vtt | 14.3 KB | ||
| 9 - Pandas Series.mp4 | 49.4 MB | ||
| 9 - pandas-series.ipynb | 46.4 KB | ||
| 7 - Python for Data Analysis Numpy English.vtt | 16.3 KB | ||
| 7 - Python for Data Analysis Numpy.mp4 | 65.3 MB | ||
| 8 - linear algebra for data science English.vtt | 12.5 KB | ||
| 8 - linear algebra for data science.mp4 | 48.3 MB | ||
| 4 - Google Colab Introduction English.vtt | 5.5 KB | ||
| 4 - Google Colab Introduction.mp4 | 13.1 MB | ||
| 5 - Anaconda Installation English.vtt | 7.3 KB | ||
| 5 - Anaconda Installation.mp4 | 33.8 MB | ||
| 6 - Jupyter Notebook English.vtt | 4.6 KB | ||
| 6 - Jupyter Notebook.mp4 | 14 MB | ||
| 68 - Python Basic Introduction English.vtt | 9.2 KB | ||
| 68 - Python Basic Introduction.mp4 | 41.2 MB | ||
| 69 - Getting Started with Python English.vtt | 14.3 KB | ||
| 69 - Getting Started with Python.mp4 | 42.3 MB | ||
| 70 - Python Data TypesNumbers English.vtt | 6.8 KB | ||
| 70 - Python Data TypesNumbers.mp4 | 22.3 MB | ||
| 71 - Python Data TypesStrings English.vtt | 10.3 KB | ||
| 71 - Python Data TypesStrings.mp4 | 26.4 MB | ||
| 72 - Python Data TypesLists English.vtt | 11.1 KB | ||
| 72 - Python Data TypesLists.mp4 | 31.6 MB | ||
| 73 - Python Data Types Dictionary English.vtt | 9.6 KB | ||
| 73 - Python Data Types Dictionary.mp4 | 23.2 MB | ||
| 74 - Python Data Types Sets English.vtt | 12.9 KB | ||
| 74 - Python Data Types Sets.mp4 | 38.8 MB | ||
| 75 - Python Loops English.vtt | 13.5 KB | ||
| 75 - Python Loops.mp4 | 41.7 MB | ||
| 76 - Decision Making English.vtt | 14 KB | ||
| 76 - Decision Making.mp4 | 44 MB | ||
| 77 - Control Statements English.vtt | 14.9 KB | ||
| 77 - Control Statements.mp4 | 42.8 MB | ||
| 78 - Python Functions English.vtt | 20.3 KB | ||
| 78 - Python Functions.mp4 | 86 MB | ||
| 64 - Underfitting Overfitting and best fitting in Machine Learning English.vtt | 7.6 KB | ||
| 64 - Underfitting Overfitting and best fitting in Machine Learning.mp4 | 48.8 MB | ||
| 65 - How to avoid Overfitting in Machine Learning English.vtt | 8.1 KB | ||
| 65 - How to avoid Overfitting in Machine Learning.mp4 | 47.4 MB | ||
| 66 - Applications of Artificial Intelligence English.vtt | 15 KB | ||
| 66 - Applications of Artificial Intelligence.mp4 | 73.5 MB | ||
| 67 - Teachable Machine English.vtt | 12.8 KB | ||
| 67 - Teachable Machine.mp4 | 37.6 MB | ||
| 63 - Project COVID19 Coronavirus Infection Probability using machine learning English.vtt | 10.2 KB | ||
| 63 - Project COVID19 Coronavirus Infection Probability using machine learning.mp4 | 44.8 MB | ||
| 62 - Fake News Detection using machine learning Python English.vtt | 9.2 KB | ||
| 62 - Fake News Detection using machine learning Python.mp4 | 35.1 MB | ||
| 58 - Customer Segmentation Part 1 English.vtt | 6 KB | ||
| 58 - Customer Segmentation Part 1.mp4 | 23.1 MB | ||
| 59 - Bank-Customer-Segmentation.ipynb | 1.2 MB | ||
| 59 - Customer Segmentation Part 2 English.vtt | 15.3 KB | ||
| 59 - Customer Segmentation Part 2.mp4 | 102.2 MB | ||
| 60 - Customer segmentation 3 English.vtt | 15.7 KB | ||
| 60 - Customer segmentation 3.mp4 | 105.5 MB | ||
| 61 - Customer Segmentation Part 4 English.vtt | 11.7 KB | ||
| 61 - Customer Segmentation Part 4.mp4 | 84.5 MB | ||
| 2 - Describe Artificial Intelligence and Machine Learning and Deep Learning English.vtt | 4.4 KB | ||
| 2 - Describe Artificial Intelligence and Machine Learning and Deep Learning.mp4 | 20.8 MB | ||
| 3 - Concept of Machine Learning English.vtt | 10.2 KB | ||
| 3 - Concept of Machine Learning.mp4 | 44.6 MB |
Complete Machine Learning & Data Science With Python | ML A-Z
https://WebToolTip.com
Last updated 6/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.13 GB | Duration: 11h 13m
Learn Numpy, Pandas, Matplotlib, Seaborn, Scipy, Supervised & Unsupervised Machine Learning A-Z and feature engineering
What you'll learn
Data Science libraries like Numpy , Pandas , Matplotlib, Scipy, Scikit Learn, Seaborn , Plotly and many more
Machine learning Concept and Different types of Machine Learning
Machine Learning Algorithms like Regression, Classification, Naive Bayes Classifier, Decision Tree, Support Vector Machine Algorithm etc..
Feature engineering
Python Basics
Requirements
No previous programming experience needed.
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